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ZENODO
Project deliverable . 2020
License: CC BY
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Project deliverable . 2020
License: CC BY
Data sources: Datacite
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EARLY WARNING AND RESPONSE SYSTEM: IMPLEMENTATION AND EVALUATION

Authors: Hess, Patrick J.;

EARLY WARNING AND RESPONSE SYSTEM: IMPLEMENTATION AND EVALUATION

Abstract

It is estimated that there are over 200,000 in-hospital cardiac arrests each year in the United States. Clinical deterioration in inpatient units often goes undetected, or if detected, it is ignored until a significant event occurs. Literature reports that with early identification and treatment, this deterioration may be prevented, decreasing the need to transfer a patient to the intensive care unit. Early warning systems (EWS) have been shown to identify patients who may experience a negative outcome as early as 20 hours before the event. A review of EWSs is included describing the three phases (early recognition, triggering a response, and appropriate response). The National Early Warning Score 2 is explored in greater detail. The purpose of this quality improvement project was to develop a guide to assist an organization in implementing an EWS. The guide includes a sample detailed project plan and evidence-based training materials. The overall framework for this project was Donabedian’s Model of Structure, Process, and Outcome. The actual implementation plan was based on Royce’s system development lifecycle. Important aspects of each of the six steps (requirement gathering, design, build, testing, deployment, and maintenance/ evolution) were explored. The proposed process was used to guide the initial phases of an implementation at a 207-bed medical center. It is essential to follow a structured implementation plan when deploying an EWS. By fully understanding the requirements iv of a proposed solution, unnecessary time and expense in an aborted implementation can be avoided.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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